OpenRaiser/Intern-Atlas huggingface dataset:
Memory-based Ensemble Learning in CMR Semantic Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Existing models typically segment either the entire 3D frame or 2D slices independently to derive clinical functional metrics from ventricular segmentation in cardiac cine sequences. While performing well overall, they struggle at the end slices. To address this, we leverage spatial continuity to extract global uncertainty from segmentation variance and use it as memory in our ensemble learning method, Streaming, for classifier weighting, balancing overall and end-slice performance. Additionally, we introduce the End Coefficient (EC) to quantify end-slice accuracy. Experiments on ACDC and M&Ms datasets show that our framework achieves near-state-of-the-art Dice Similarity Coefficient (DSC) and outperforms all models on end-slice performance, improving patient-specific segmentation accuracy.
Predicting Drive Test Results in Mobile Networks Using Optimization Techniques
Venue: arXiv cs.NI 2025
Year: 2025

Mobile network operators constantly optimize their networks to ensure superior service quality and coverage. This optimization is crucial for maintaining an optimal user experience and requires extensive data collection and analysis. One of the primary methods for gathering this data is through drive tests, where technical teams use specialized equipment to collect signal information across various regions. However, drive tests are both costly and time-consuming, and they face challenges such as traffic conditions, environmental factors, and limited access to certain areas. These constraints make it difficult to replicate drive tests under similar conditions. In this study, we propose a method that enables operators to predict received signal strength at specific locations using data from other drive test points. By reducing the need for widespread drive tests, this approach allows operators to save time and resources while still obtaining the necessary data to optimize their networks and mitigate the challenges associated with traditional drive tests.
The Accuracy Cost of Weakness: A Theoretical Analysis of Fixed-Segment Weak Labeling for Events in Time
Venue: arXiv cs.LG 2025
Year: 2025

Accurate labels are critical for deriving robust machine learning models. Labels are used to train supervised learning models and to evaluate most machine learning paradigms. In this paper, we model the accuracy and cost of a common weak labeling process where annotators assign presence or absence labels to fixed-length data segments for a given event class. The annotator labels a segment as "present" if it sufficiently covers an event from that class, e.g., a birdsong sound event in audio data. We analyze how the segment length affects the label accuracy and the required number of annotations, and compare this fixed-length labeling approach with an oracle method that uses the true event activations to construct the segments. Furthermore, we quantify the gap between these methods and verify that in most realistic scenarios the oracle method is better than the fixed-length labeling method in both accuracy and cost. Our findings provide a theoretical justification for adaptive weak labeling strategies that mimic the oracle process, and a foundation for optimizing weak labeling processes in sequence labeling tasks.
Generalizable Reinforcement Learning with Biologically Inspired Hyperdimensional Occupancy Grid Maps for Exploration and Goal-Directed Path Planning
Venue: arXiv cs.RO 2025
Year: 2025

Real-time autonomous systems utilize multi-layer computational frameworks to perform critical tasks such as perception, goal finding, and path planning. Traditional methods implement perception using occupancy grid mapping (OGM), segmenting the environment into discretized cells with probabilistic information. This classical approach is well-established and provides a structured input for downstream processes like goal finding and path planning algorithms. Recent approaches leverage a biologically inspired mathematical framework known as vector symbolic architectures (VSA), commonly known as hyperdimensional computing, to perform probabilistic OGM in hyperdimensional space. This approach, VSA-OGM, provides native compatibility with spiking neural networks, positioning VSA-OGM as a potential neuromorphic alternative to conventional OGM. However, for large-scale integration, it is essential to assess the performance implications of VSA-OGM on downstream tasks compared to established OGM methods. This study examines the efficacy of VSA-OGM against a traditional OGM approach, Bayesian Hilbert Maps (BHM), within reinforcement learning based goal finding and path planning frameworks, across a controlled exploration environment and an autonomous driving scenario inspired by the F1-Tenth challenge. Our results demonstrate that VSA-OGM maintains comparable learning performance across single and multi-scenario training configurations while improving performance on unseen environments by approximately 47%. These findings highlight the increased generalizability of policy networks trained with VSA-OGM over BHM, reinforcing its potential for real-world deployment in diverse environments.
A 3D Facial Reconstruction Evaluation Methodology: Comparing Smartphone Scans with Deep Learning Based Methods Using Geometry and Morphometry Criteria
Venue: arXiv cs.CV 2025
Year: 2025

Three-dimensional (3D) facial shape analysis has gained interest due to its potential clinical applications. However, the high cost of advanced 3D facial acquisition systems limits their widespread use, driving the development of low-cost acquisition and reconstruction methods. This study introduces a novel evaluation methodology that goes beyond traditional geometry-based benchmarks by integrating morphometric shape analysis techniques, providing a statistical framework for assessing facial morphology preservation. As a case study, we compare smartphone-based 3D scans with state-of-the-art deep learning reconstruction methods from 2D images, using high-end stereophotogrammetry models as ground truth. This methodology enables a quantitative assessment of global and local shape differences, offering a biologically meaningful validation approach for low-cost 3D facial acquisition and reconstruction techniques.
Dual Formulation for Non-Rectangular Lp Robust Markov Decision Processes
Venue: arXiv cs.AI 2025
Year: 2025

We study robust Markov decision processes (RMDPs) with non-rectangular uncertainty sets, which capture interdependencies across states unlike traditional rectangular models. While non-rectangular robust policy evaluation is generally NP-hard, even in approximation, we identify a powerful class of $L_p$-bounded uncertainty sets that avoid these complexity barriers due to their structural simplicity. We further show that this class can be decomposed into infinitely many \texttt{sa}-rectangular $L_p$-bounded sets and leverage its structural properties to derive a novel dual formulation for $L_p$ RMDPs. This formulation provides key insights into the adversary's strategy and enables the development of the first robust policy evaluation algorithms for non-rectangular RMDPs. Empirical results demonstrate that our approach significantly outperforms brute-force methods, establishing a promising foundation for future investigation into non-rectangular robust MDPs.
Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects
Venue: arXiv cs.NE 2025
Year: 2025

Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking Neural Networks (SNNs) excel in handling such data with high efficiency, owing to their rich neuronal dynamics and sparse activity patterns. Given the recent surge in the development of SNNs, there is an urgent need for a comprehensive evaluation of their temporal processing capabilities. In this paper, we first conduct an in-depth assessment of commonly used neuromorphic benchmarks, revealing critical limitations in their ability to evaluate the temporal processing capabilities of SNNs. To bridge this gap, we further introduce a benchmark suite consisting of three temporal processing tasks characterized by rich temporal dynamics across multiple timescales. Utilizing this benchmark suite, we perform a thorough evaluation of recently introduced SNN approaches to elucidate the current status of SNNs in temporal processing. Our findings indicate significant advancements in recently developed spiking neuron models and neural architectures regarding their temporal processing capabilities, while also highlighting a performance gap in handling long-range dependencies when compared to state-of-the-art non-spiking models. Finally, we discuss the key challenges and outline potential avenues for future research.
Balancing physical modeling and musical requirements: Algorithmically simulating the calls of Hyalessa maculaticollis for real-time instrumental control
Venue: arXiv cs.SD 2025
Year: 2025

This paper presents an algorithm that simulates the calls of the Hyalessa maculaticollis cicada for musical use. Written in SuperCollider, its input parameters enable real-time control of the insect call phase, loudness, and perceived musical pitch. To this end, the anatomical mechanics of the tymbal muscles, tymbal apodeme, tymbal ribs, tymbal plate, abdominal air sac, tympana, and opercula are physically modeled. This also includes decoherence, following the hypothesis that it, in H. maculaticollis, might explain the change in timbre apparent during the final phase of a call sequence. Overall, the algorithm seems to illustrate three main points regarding the trade-offs encountered when modeling bioacoustics for tonal use: that it may be necessary to prioritize musical requirements over realistic physical modeling at many stages of design and implementation; that the resulting adjustments may revolve around having physical modeling perceptually yield sonic events that are well-pitched, single-attack, single-source, and timbrally expressive; that the pitch-adjusted simulation of resonating bodies may fail musically precisely when it succeeds physically, by inducing the perception of different sound sources for different pitches. Audio examples are included, and the source code is structured and documented so as to support the further development of cicada bioacoustics for musical use.
SQ-GAN: Semantic Image Communications Using Masked Vector Quantization
Venue: arXiv cs.CV 2025
Year: 2025

This work introduces Semantically Masked Vector Quantized Generative Adversarial Network (SQ-GAN), a novel approach integrating semantically driven image coding and vector quantization to optimize image compression for semantic/task-oriented communications. The method only acts on source coding and is fully compliant with legacy systems. The semantics is extracted from the image computing its semantic segmentation map using off-the-shelf software. A new specifically developed semantic-conditioned adaptive mask module (SAMM) selectively encodes semantically relevant features of the image. The relevance of the different semantic classes is task-specific, and it is incorporated in the training phase by introducing appropriate weights in the loss function. SQ-GAN outperforms state-of-the-art image compression schemes such as JPEG2000, BPG, and deep-learning based methods across multiple metrics, including perceptual quality and semantic segmentation accuracy on the reconstructed image, at extremely low compression rates.
Robust Learning of Multi-index Models via Iterative Subspace Approximation
Venue: arXiv cs.LG 2025
Year: 2025

We study the task of learning Multi-Index Models (MIMs) with label noise under the Gaussian distribution. A $K$-MIM is any function $f$ that only depends on a $K$-dimensional subspace. We focus on well-behaved MIMs with finite ranges that satisfy certain regularity properties. Our main contribution is a general robust learner that is qualitatively optimal in the Statistical Query (SQ) model. Our algorithm iteratively constructs better approximations to the defining subspace by computing low-degree moments conditional on the projection to the subspace computed thus far, and adding directions with relatively large empirical moments. This procedure efficiently finds a subspace $V$ so that $f(\mathbf{x})$ is close to a function of the projection of $\mathbf{x}$ onto $V$. Conversely, for functions for which these conditional moments do not help, we prove an SQ lower bound suggesting that no efficient learner exists. As applications, we provide faster robust learners for the following concept classes: * {\bf Multiclass Linear Classifiers} We give a constant-factor approximate agnostic learner with sample complexity $N = O(d) 2^{\mathrm{poly}(K/ε)}$ and computational complexity $\mathrm{poly}(N ,d)$. This is the first constant-factor agnostic learner for this class whose complexity is a fixed-degree polynomial in $d$. * {\bf Intersections of Halfspaces} We give an approximate agnostic learner for this class achieving 0-1 error $K \tilde{O}(\mathrm{OPT}) + ε$ with sample complexity $N=O(d^2) 2^{\mathrm{poly}(K/ε)}$ and computational complexity $\mathrm{poly}(N ,d)$. This is the first agnostic learner for this class with near-linear error dependence and complexity a fixed-degree polynomial in $d$. Furthermore, we show that in the presence of random classification noise, the complexity of our algorithm scales polynomially with $1/ε$.
Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages
Venue: arXiv cs.CL 2025
Year: 2025

Recent advances in generative AI have precipitated a proliferation of novel writing assistants. These systems typically rely on multilingual large language models (LLMs), providing globalized workers the ability to revise or create diverse forms of content in different languages. However, there is substantial evidence indicating that the performance of multilingual LLMs varies between languages. Users who employ writing assistance for multiple languages are therefore susceptible to disparate output quality. Importantly, recent research has shown that people tend to generalize algorithmic errors across independent tasks, violating the behavioral axiom of choice independence. In this paper, we analyze whether user utilization of novel writing assistants in a charity advertisement writing task is affected by the AI's performance in a second language. Furthermore, we quantify the extent to which these patterns translate into the persuasiveness of generated charity advertisements, as well as the role of peoples' beliefs about LLM utilization in their donation choices. Our results provide evidence that writers who engage with an LLM-based writing assistant violate choice independence, as prior exposure to a Spanish LLM reduces subsequent utilization of an English LLM. While these patterns do not affect the aggregate persuasiveness of the generated advertisements, people's beliefs about the source of an advertisement (human versus AI) do. In particular, Spanish-speaking female participants who believed that they read an AI-generated advertisement strongly adjusted their donation behavior downwards. Furthermore, people are generally not able to adequately differentiate between human-generated and LLM-generated ads. Our work has important implications for the design, development, integration, and adoption of multilingual LLMs as assistive agents -- particularly in writing tasks.
Entropy Collapse in Mobile Sensors: The Hidden Risks of Sensor-Based Security
Venue: arXiv cs.CR 2025
Year: 2025

Mobile sensor data has been proposed for security-critical applications such as device pairing, proximity detection, and continuous authentication. However, the foundational premise that these signals provide sufficient entropy remains under-explored. In this work, we systematically analyse the entropy of mobile sensor data using four datasets from multiple application contexts (UCI-HAR, SHL, Relay, and PerilZIS). Using direct computation and estimation, we report entropy values (max, Shannon, collision, and min-entropy) for an exhaustive range of sensor combinations. We demonstrate that the entropy of mobile sensors remains far below what is considered secure by modern standards for security applications, even when many sensors are combined. In particular, we observe an alarming divergence between average-case Shannon entropy and worst-case min-entropy. Single-sensor min-entropy varies between 3.408-4.483 bits despite Shannon entropy being several multiples higher. We also show that redundancies between sensor modalities contribute to a ~75% reduction between Shannon and min-entropy. Indeed, min-entropy plateaus between 8.1-23.9 bits when combining up to 22 modalities, while Shannon entropy can exceed 80 bits. Adding sensors typically increases Shannon entropy but moves min-entropy by only ~1-2 bits per added modality, evidencing entropy collapse under redundancy. Our results reveal that adversaries may feasibly predict sensor signals through an exhaustive exploration of the measurement space. Our work also calls into question the widely held assumption that adding more sensors inherently yields higher security. Ultimately, we strongly urge caution when relying on mobile sensor data for security applications.
Zero-shot generation of synthetic neurosurgical data with large language models
Venue: arXiv cs.CL 2025
Year: 2025

Clinical data is fundamental to advance neurosurgical research, but access is often constrained by data availability, small sample sizes, privacy regulations, and resource-intensive preprocessing and de-identification procedures. Synthetic data offers a potential solution to challenges associated with accessing and using real-world data (RWD). This study aims to evaluate the capability of zero-shot generation of synthetic neurosurgical data with a large language model (LLM), GPT-4o, by benchmarking with the conditional tabular generative adversarial network (CTGAN). Synthetic datasets were compared to real-world neurosurgical data to assess fidelity (means, proportions, distributions, and bivariate correlations), utility (ML classifier performance on RWD), and privacy (duplication of records from RWD). The GPT-4o-generated datasets matched or exceeded CTGAN performance, despite no fine-tuning or access to RWD for pre-training. Datasets demonstrated high univariate and bivariate fidelity to RWD without directly exposing any real patient records, even at amplified sample size. Training an ML classifier on GPT-4o-generated data and testing on RWD for a binary prediction task showed an F1 score (0.706) with comparable performance to training on the CTGAN data (0.705) for predicting postoperative functional status deterioration. GPT-4o demonstrated a promising ability to generate high-fidelity synthetic neurosurgical data. These findings also indicate that data synthesized with GPT-4o can effectively augment clinical data with small sample sizes, and train ML models for prediction of neurosurgical outcomes. Further investigation is necessary to improve the preservation of distributional characteristics and boost classifier performance.
Optimizing GPT for Video Understanding: Zero-Shot Performance and Prompt Engineering
Venue: arXiv cs.CV 2025
Year: 2025

In this study, we tackle industry challenges in video content classification by exploring and optimizing GPT-based models for zero-shot classification across seven critical categories of video quality. We contribute a novel approach to improving GPT's performance through prompt optimization and policy refinement, demonstrating that simplifying complex policies significantly reduces false negatives. Additionally, we introduce a new decomposition-aggregation-based prompt engineering technique, which outperforms traditional single-prompt methods. These experiments, conducted on real industry problems, show that thoughtful prompt design can substantially enhance GPT's performance without additional finetuning, offering an effective and scalable solution for improving video classification.
From Argumentation to Deliberation: Perspectivized Stance Vectors for Fine-grained (Dis)agreement Analysis
Venue: arXiv cs.CL 2025
Year: 2025

Debating over conflicting issues is a necessary first step towards resolving conflicts. However, intrinsic perspectives of an arguer are difficult to overcome by persuasive argumentation skills. Proceeding from a debate to a deliberative process, where we can identify actionable options for resolving a conflict requires a deeper analysis of arguments and the perspectives they are grounded in - as it is only from there that one can derive mutually agreeable resolution steps. In this work we develop a framework for a deliberative analysis of arguments in a computational argumentation setup. We conduct a fine-grained analysis of perspectivized stances expressed in the arguments of different arguers or stakeholders on a given issue, aiming not only to identify their opposing views, but also shared perspectives arising from their attitudes, values or needs. We formalize this analysis in Perspectivized Stance Vectors that characterize the individual perspectivized stances of all arguers on a given issue. We construct these vectors by determining issue- and argument-specific concepts, and predict an arguer's stance relative to each of them. The vectors allow us to measure a modulated (dis)agreement between arguers, structured by perspectives, which allows us to identify actionable points for conflict resolution, as a first step towards deliberation.
Mind What You Ask For: Emotional and Rational Faces of Persuasion by Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Be careful what you ask for, you just might get it. This saying fits with the way large language models (LLMs) are trained, which, instead of being rewarded for correctness, are increasingly rewarded for pleasing the recipient. So, they are increasingly effective at persuading us that their answers are valuable. But what tricks do they use in this persuasion? In this study, we examine what are the psycholinguistic features of the responses used by twelve different language models. By grouping response content according to rational or emotional prompts and exploring social influence principles employed by LLMs, we ask whether and how we can mitigate the risks of LLM-driven mass misinformation. We position this study within the broader discourse on human-centred AI, emphasizing the need for interdisciplinary approaches to mitigate cognitive and societal risks posed by persuasive AI responses.
Evaluating GPT's Capability in Identifying Stages of Cognitive Impairment from Electronic Health Data
Venue: arXiv cs.LG 2025
Year: 2025

Identifying cognitive impairment within electronic health records (EHRs) is crucial not only for timely diagnoses but also for facilitating research. Information about cognitive impairment often exists within unstructured clinician notes in EHRs, but manual chart reviews are both time-consuming and error-prone. To address this issue, our study evaluates an automated approach using zero-shot GPT-4o to determine stage of cognitive impairment in two different tasks. First, we evaluated the ability of GPT-4o to determine the global Clinical Dementia Rating (CDR) on specialist notes from 769 patients who visited the memory clinic at Massachusetts General Hospital (MGH), and achieved a weighted kappa score of 0.83. Second, we assessed GPT-4o's ability to differentiate between normal cognition, mild cognitive impairment (MCI), and dementia on all notes in a 3-year window from 860 Medicare patients. GPT-4o attained a weighted kappa score of 0.91 in comparison to specialist chart reviews and 0.96 on cases that the clinical adjudicators rated with high confidence. Our findings demonstrate GPT-4o's potential as a scalable chart review tool for creating research datasets and assisting diagnosis in clinical settings in the future.